Analyzing moderately large data sets


 Francis Freeman
 3 years ago
 Views:
Transcription
1 Analyzing moderately large data sets Thomas Lumley R Core Development Team user Rennes
2 Problem Analyzing a data set that contains N observations on P variables, where N P is inconveniently large (from 100Mb on small computers up to many Gb). I will mostly assume that any tasks can be broken down into pieces that use only p variables, where p is small. Since N is assumed large, only tasks with complexity (nearly) linear in N are feasible. Mixture of concrete implementations for linear models, general ideas.
3 Outline Technologies Connections netcdf SQL Memory profiling Strategies Load only the necessary variables Incremental boundedmemory algorithms Randomized subset algorithms Outsource the computing to SQL
4 Examples Linear regression and subset selection on large data sets (large N, small P ) Analysis of large surveys (N , P 500, p 10 ) Wholegenome association studies (N 5000, P 10 6, p 1) Wholegenome data cleaning (P 5000, N 10 6, p 1)
5 File connections file() opens a connection to a file. read.table() and friends can read incrementally from the connection read 1000 records process them lather, rinse, repeat Examples: summaryrprof(), read.fwf() Chunk size should be small enough not to cause disk thrashing, large enough that the fixed costs of read.table() don t matter. Empirically, performance is constant over a fairly wide range.
6 File connections Since read.table() is the bottleneck, make sure to use it efficiently Specify correct types in colclasses= argument to get faster conversions Specify "NULL" for columns that you don t need to read.
7 netcdf netcdf is an arraystructured storage format developed for atmospheric science research (HDF5 from NCSA is similar, but R support is not as good). Allows efficient reading of rows, columns, layers, general hyperrectangles of data. We have used this for wholegenome genetic data. Martin Morgan (FHCRC) reports that this approach to wholegenome data scales to a cluster with hundreds of nodes.
8 Large data netcdf was designed by the NSFfunded UCAR consortium, who also manage the National Center for Atmospheric Research. Atmospheric data are often arrayoriented: eg temperature, humidity, wind speed on a regular grid of (x, y, z, t). Need to be able to select rectangles of data eg range of (x, y, z) on a particular day t. Because the data are on a regular grid, the software can work out where to look on disk without reading the whole file: efficient data access.
9 WGA Array oriented data (position on genome, sample number) for genotypes, probe intensities. Potentially very large data sets: 2,000 people 300,000 = tens of Gb 16,000 people 1,000,000 SNPs = hundreds of Gb. Even worse after imputation to 2,500,000 SNPs. R can t handle a matrix with more than billion entries even if your computer has memory for it. Even data for one chromosome may be too big.
10 Using netcdf data With the ncdf package: open.ncdf() opens a netcdf file and returns a connection to the file (rather than loading the data) get.var.ncdf() retrieves all or part of a variable. close.ncdf() closes the connection to the file.
11 Dimensions Variables can use one or more array dimensions of a file!%&'()$!"#$ *)+,.')/$ 012,&,/,&)$
12 Example Finding long homozygous runs (possible deletions) nc < open.ncdf("hapmap.nc") ## read all of chromosome variable chromosome < get.var.ncdf(nc, "chr", start=1, count=1) ## set up list for results runs<vector("list", nsamples) for(i in 1:nsamples}{ ## read all genotypes for one person genotypes < get.var.ncdf(nc, "geno", start=c(1,i),count=c(1,1)) ## zero for htzygous, chrm number for hmzygous hmzygous < genotypes!= 1 hmzygous < as.vector(hmzygous*chromosome)
13 Example } ## consecutive runs of same value r < rle(hmzygous) begin < cumsum(r$lengths) end < cumsum(c(1, r$lengths)) long < which ( r$lengths > 250 & r$values!=0) runs[[i]] < cbind(begin[long], end[long], r$lengths[long]) close.ncdf(nc) Notes chr uses only the SNP dimension, so start and count are single numbers geno uses both SNP and sample dimensions, so start and count have two entries. rle compresses runs of the same value to a single entry.
14 Creating netcdf files Creating files is more complicated Define dimensions Define variables and specify which dimensions they use Create an empty file Write data to the file.
15 Dimensions Specify the name of the dimension, the units, and the allowed values in the dim.def.ncdf function. One dimension can be unlimited, allowing expansion of the file in the future. An unlimited dimension is important, otherwise the maximum variable size is 2Gb. snpdim<dim.def.ncdf("position","bases", positions) sampledim<dim.def.ncdf("seqnum","count",1:10, unlim=true)
16 Variables Variables are defined by name, units, and dimensions varchrm < var.def.ncdf("chr","count",dim=snpdim, missval=1, prec="byte") varsnp < var.def.ncdf("snp","rs",dim=snpdim, missval=1, prec="integer") vargeno < var.def.ncdf("geno","base",dim=list(snpdim, sampledim), missval=1, prec="byte") vartheta < var.def.ncdf("theta","deg",dim=list(snpdim, sampledim), missval=1, prec="double") varr < var.def.ncdf("r","copies",dim=list(snpdim, sampledim), missval=1, prec="double")
17 Creating the file The file is created by specifying the file name ad a list of variables. genofile<create.ncdf("hapmap.nc", list(varchrm, varsnp, vargeno, vartheta, varr)) The file is empty when it is created. Data can be written using put.var.ncdf(). Because the whole data set is too large to read, we might read raw data and save to netcdf for one person at a time. for(i in 1:4000){ geno<readrawdata(i) ## somehow put.var.ncdf(genofile, "geno", genc, start=c(1,i), count=c(1,1)) }
18 SQL Relational databases are the natural habitat of many large data sets. They are good at Data management Merging data sets Selecting subsets Simple data summaries Relational databases all speak Structured Query Language (SQL), which is more or less standardized. Most of the complications of SQL deal with transaction processing, which we don t have. Initially we care about the SELECT statement.
19 SQL SELECT var1, var2 FROM table WHERE condition SELECT sum(var1) as svar1, sum(var2) as svar2 FROM table GROUP BY category SELECT * FROM table limit 1 SELECT count(*) FROM table SELECT count(*) FROM table GROUP BY category
20 SQL DBI package defines an interface to databases, implemented by eg RSQLite. RODBC defines a similar but simpler interface to ODBC (predominantly a Windows standard interface). SQLite ( via RSQLite or sqldf, is the easiest database to use. It isn t suitable for very large databases or for large numbers of simultaneous users, but it is designed to be used as embedded storage for other programs. Gabor Grothendieck s sqldf package makes it easier to load data into SQLite and manipulate it in R.
21 DBI/RSQLite library(rsqlite) sqlite < dbdriver("sqlite") conn < dbconnect(sqlite, "~/CHIS/brfss07.db") dbgetquery(conn, "select count(*) from brfss") results < dbsendquery(conn, "select HSAGEIR from brfss") fetch(results,10) fetch(results,10) dbclearresult(result) Change the dbdriver() call to connect to a different database program (eg Postgres).
22 Memory profiling When memory profiling is compiled into R tracemem(object) reports all copying of object Rprof(,memory.profling=TRUE) writes information on duplications, large memory allocations, new heap pages at each tick of the timesampling profiler Rprofmem() is a pure allocation profiler: it writes a stack trace for any large memory allocation or any new heap page. Bioconductor programmers have used tracemem() to cut down copying for single large data objects. Rprofmem() showed that foreign::read.dta() had a bottleneck in as.data.frame.list(), which copies each column 10 times.(!)
23 Loadondemand Use case: large surveys. The total data set size is large (eg N 10 5, P 10 3 ), but any given analysis uses only p 10 variables. Loading the whole data set is feasible on 64bit systems, but not on my laptop. We want an R object that represents the data set and can load the necessary variables when they are needed. Difficult in general: data frames work because they are specialcased by the internal code, especially model.frame() Formula + data.frame interface is tractable, as is formula + expression.
24 Basic idea dosomething < function(formula, database){ varlist < paste( all.vars(formula), collapse=", ") query < paste("select",varlist,"from",database$tablename) dataframe < dbgetquery(database$connection, query) ## now actually do Something fitmodel(formula, dataframe) } First construct a query to load all the variables you need, then submit it to the database to get the data frame, then proceed as usual. Refinements: some variables may be in memory, not in the database, we may need to define new variables, we may want to wrap an existing set of code.
25 Wrapping existing code Define a generic function to dispatch on the second (data) argument dosomething < function(formula, data,...){ UseMethod("doSomething", data) } and set the existing function as the default method dosomething.database < (formula, database,...){ varlist < paste( all.vars(formula), collapse=", ") query < paste("select",varlist,"from",database$tablename) dataframe < dbgetquery(database$connection, query) ## now actually do Something dosomething(formula, dataframe,...) }
26 Allowing variables in memory To allow the function to pick up variables from memory, just restrict the database query to variables that are in the database dbvars < names(dbgetquery(conn, "select * from table limit 1")) formulavars < all.vars(formula) varlist < paste( setdiff(formulavars, dbvars), collapse=", ") [In practice we would find the list of names in the database first and cache it in an R object] Now model.frame() will automatically pick up variables in memory, unless they are masked by variables in the database table the same situation as for data frames.
27 Allowing updates Three approaches: Write new variables into the database with SQL code: needs permission, reference semantics, restricted to SQL syntax Create new variables in memory and save to the database: needs permission, reference semantics, high network traffic Store the expressions and create new variables on data load: wasted effort The third strategy wins when data loading is the bottleneck and the variable will eventually have to be loaded into R.
28 Design A database object stores the connection, table name, new variable information New variables are created with the update method mydata < update(mydata, avgchol = (chol1 + chol2)/2, hibp = (systolic>140) (diastolic>90) ) An expression can use variables in the database or previously defined ones, but not simultaneously defined ones. Multiple update()s give a stack of lists of expressions Use all.vars going down the stack to find which variables to query Return up the stack making the necessary transformations with eval() Implemented in survey, mitools packages.
29 Object structure survey.design objects contain a data frame of variables, and a couple of matrices of survey design metadata. DBIsvydesign contain the survey metadata and database information: connection, table name, driver name, database name. All analysis functions are already generic, dispatching on the type of survey metadata (replicate weights vs strata and clusters). Define methods for DBIsvydesign that load the necessary variables into the $variables slot and call NextMethod to do the work Ideally we would just override model.frame(), but it is already generic on its first argument, probably a mistake in hindsight.
30 Object structure > svymean function (x, design, na.rm = FALSE,...) {.svycheck(design) UseMethod("svymean", design) } > survey:::svymean.dbisvydesign function (x, design,...) { design$variables < getvars(x, design$db$connection, design$db$tablename, updates = design$updates) NextMethod("svymean", design) }
31 Incremental algorithms: biglm Linear regression takes O(np 2 +p 3 ) time, which can t be reduced easily (for large p you can replace p 3 by p log 2 7, but not usefully). The R implementation takes O(np+p 2 ) memory, but this can be reduced dramatically by constructing the model matrix in chunks. Compute X T X and X T y in chunks and use ˆβ = (X T X) 1 X T y Compute the incremental QR decomposition of X to get R and Q T Y, solve Rβ = Q T y The second approach is slightly slower but more accurate. Coding it would be a pain, but Alan Miller has done it.(applied Statistics algorithm 274) Fortran subroutine INCLUD in biglm and in leaps updates R and Q T Y by adding one new observation.
32 Incremental algorithms: biglm The only new methodology in biglm is an incremental computation for the Huber/White sandwich variance estimator. The middle term in the sandwich is n i=1 x i (y i x iˆβ) 2 x T i and since ˆβ is not known in advance this appears to require a second pass through the data. In fact, we can accumulate a (p+1) 2 (p+1) 2 matrix of products of x and y that is sufficient to compute the sandwich estimator without a second pass.
33 biglm: interface biglm() uses the traditional formula/ data.frame interface to set up a model update() adds a new chunk of data to the model. The model matrices have to be compatible, so datadependent terms are not allowed. Factors must have their full set of levels specified (not necessarily present in the data chunk), splines must use explicitly specified knots, etc. One subtle issue is checking for linear dependence. This can t be done until the end, because the next chunk of data could fix the problem. The checking is done by the coef() and vcov() methods.
34 bigglm Generalized linear models require iteration, so the same data has to be used repeatedly. The bigglm() function needs a data source that can be reread during each iteration. The basic code is in bigglm.function(). It iterates over calls to biglm, and uses a data() function with one argument data(reset=false) supplies the next chunk of data or NULL or a zerorow data frame if there is no more. data(reset=true) resets to the start of the data set, for the next iteration.
35 bigglm and SQL One way to write the data() function is to load data from a SQL query. The query needs to be prepared once, in the same way as for loadondemand databases. modelvars < all.vars(formula) dots < as.list(substitute(list(...)))[1] dotvars < unlist(lapply(dots, all.vars)) vars < unique(c(modelvars, dotvars)) tablerow < dbgetquery(data, paste("select * from ", tablename, " limit 1")) tablevars < vars[vars %in% names(tablerow)] query < paste("select ", paste(tablevars, collapse = ", "), " from ", tablename)
36 bigglm and SQL The query is resent when reset=true and otherwise a chunk of the result set is loaded. chunk < function(reset = FALSE) { if (reset) { if (got > 0) { dbclearresult(result) result << dbsendquery(data, query) got << 0 } return(true) } rval < fetch(result, n = chunksize) got << got + nrow(rval) if (nrow(rval) == 0) return(null) return(rval) }
37 bigglm iterations If p is not too large and the data are reasonably wellbehaved so that the loglikelihood is wellapproximated by a quadratic, three iterations should be sufficient and good starting values will cut this to two iterations or even to one. Good starting values may be available by fitting the model to a subsample. If p is large or the data are sparse it may be preferable to use a fixed small number of iterations as a regularization technique.
38 Subset selection and biglm Forward/backward/exhaustive search algorithms for linear models need only the fitted QR decomposition or matrix of sums and products, so forward search is O(np 2 + p 3 + p 2 ) and exhaustive search is O(np 2 + p p ) In particular, the leaps package uses the same AS274 QR decomposition code and so can easily work with biglm The subset selection code does not have to fit all the regression models, only work out the residual sum of squares for each model. The user has to fit the desired models later; the package has vcov() and coef() methods.
39 Subset selection and glm For a generalized linear model it is not possible to compute the loglikelihood without a full model fit, so the subset selection shortcuts do not work. They do work approximately, which is good enough for many purposes. Fit the maximal model with bigglm Do subset selection on the QR decomposition object, which represents the last iteration of weighted least squares, returning several models of each size (eg nbest=10) Refit the best models with bigglm to compute the true loglikelihood, AIC, BIC, CIC, DIC,...,MIC(key mouse). joke (c) Scott Zeger
40 Random subsets Even when analyzing a random subset of the data is not enough for final results it can be a useful step. Fitting a glm to more than N observations and then taking one iteration from these starting values for the full data is asymptotically equivalent to the MLE. Computing a 1 ɛ confidence interval for the median on a subsample, followed up by one pass through the data will be successful with probability 1 ɛ; otherwise repeat until successful. The same trick works for quantile regression. Averaging an estimator over a lot of random subsamples will be valid and close to efficient for any nconsistent estimator if the subsamples are large enough for the firstorder term in the sampling error to dominate.
41 Median Take a subset at random of size n N Construct a 99.99% confidence interval (l, u) for the median. The interval will cover about 2/ n fraction of your data. Take one pass through the data, counting how many observations m l are below l and how many m u above u, and loading any that are between l and u. This takes about 2N/ n memory If m l < N/2 and m u < N/2 the median is the N/2 m l th element of those loaded into memory, and this happens with probability 99.99%. Otherwise repeat until it works (average number of trials is ).
42 Median Total memory use is n + 2N/ n, optimized by n = N 2/3. With N = 10 6, n = 10 4, ie, 99% reduction If the data are already in random order we can use the first n for the random subsample, so a total of only one pass is needed. Otherwise need two passes. Potentially large improvement over sorting, which takes lg N lg n passes to sort N items with working memory that holds n. For quantile regression the same idea works with n = (Np) 2/3. See Roger Koenker s book.
43 Twophase sampling For survival analysis and logistic regression, can do much better than random sampling. When studying a rare event, most of the information comes from the small number of cases. The case cohort design uses the cases and a stratified random sample of noncases, to achieve nearly full efficiency at much reduced computational effort. Used in analyzing large national health databases in Scandinavia. Analyse using cch() in the survival package.
44 Outsourcing to SQL SQL can do addition and multiplication within a record and sums across records This means it can construct the design matrix X and X T X and X T y for a linear regression of y on X R can then do the last step of matrix inversion and multiplication to compute ˆβ. This approach is boundedmemory and also bounded data transfer (if the database is on a remote computer). There is no upper limit on data set size except limits on the database server.
45 Model frame and model matrix The structure of the design matrix does not depend on the data values and so can be computed by model.frame() and model.matrix() even from a zerorow version of the data. Current implementation allows only contr.treatment contrasts, and interactions are allowed only between factor variables. Model frame and model matrix are created as new tables in the database (views might be better), and a finalizer is registered to drop the tables when the objects are garbagecollected. Subsetting is handled by using zero weight for observations outside the subset. Not the most efficient implementation, but never modifies tables and has passbyvalue semantics.
46 Model frame and model matrix > source("sqlm.r") > sqlapi<sqldataset("api.db",table.name="apistrat",weights="pw") Loading required package: DBI Warning message: In is.na(rows) : is.na() applied to non(list or vector) of type NULL > s1<sqllm(api00~api99+emer+stype,sqlapi) > s1 _api99 _emer stypeh stypem _Intercept_ attr(,"var") _api99 _emer stypeh stypem _Intercept api _emer stypeh stypem _Intercept_ > sqrt(diag(attr(s1,"var"))) _api99 _emer stypeh stypem _Intercept_
47 Model frame and model matrix > dblisttables(sqlapi$conn) [1] "_mf_10d63af1" "_mm_60b7acd9" "apiclus1" "apiclus2" "apistrat" > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells Vcells > dblisttables(sqlapi$conn) [1] "apiclus1" "apiclus2" "apistrat"
48 Other models Chen & Ripley (DSC 2003) described how to use this approach more generally: the iterative optimization for many models can be written as arithmetic on large data sets and parameters to create a small update vector, followed by a small amount of computation to get new parameter values. For example, fitting a glm by IWLS involves repeated linear model fits, with the working weights and working response recomputed after each fit. In a dialect of SQL that has EXP() the working weights and working response can be computed by a select statement.
Storing and retrieving large data
Storing and retrieving large data Thomas Lumley Ken Rice UW Biostatistics Seattle, June 2009 Large data R is well known to be unable to handle large data sets. Solutions: Get a bigger computer: Linux computer
More information9. Handling large data
9. Handling large data Thomas Lumley Ken Rice Universities of Washington and Auckland Seattle, June 2011 Large data R is well known to be unable to handle large data sets. Solutions: Get a bigger computer:
More information7. Working with Big Data
7. Working with Big Data Thomas Lumley Ken Rice Universities of Washington and Auckland Lausanne, September 2014 Large data R is well known to be unable to handle large data sets. Solutions: Get a bigger
More informationYour Best Next Business Solution Big Data In R 24/3/2010
Your Best Next Business Solution Big Data In R 24/3/2010 Big Data In R R Works on RAM Causing Scalability issues Maximum length of an object is 2^311 Some packages developed to help overcome this problem
More informationBig Data and Parallel Work with R
Big Data and Parallel Work with R What We'll Cover Data Limits in R Optional Data packages Optional Function packages Going parallel Deciding what to do Data Limits in R Big Data? What is big data? More
More informationSTATISTICA Formula Guide: Logistic Regression. Table of Contents
: Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 SigmaRestricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary
More informationINTRODUCTION TO SURVEY DATA ANALYSIS THROUGH STATISTICAL PACKAGES
INTRODUCTION TO SURVEY DATA ANALYSIS THROUGH STATISTICAL PACKAGES Hukum Chandra Indian Agricultural Statistics Research Institute, New Delhi110012 1. INTRODUCTION A sample survey is a process for collecting
More informationScalable Data Analysis in R. Lee E. Edlefsen Chief Scientist UserR! 2011
Scalable Data Analysis in R Lee E. Edlefsen Chief Scientist UserR! 2011 1 Introduction Our ability to collect and store data has rapidly been outpacing our ability to analyze it We need scalable data analysis
More informationAPPM4720/5720: Fast algorithms for big data. Gunnar Martinsson The University of Colorado at Boulder
APPM4720/5720: Fast algorithms for big data Gunnar Martinsson The University of Colorado at Boulder Course objectives: The purpose of this course is to teach efficient algorithms for processing very large
More informationLecture 25: Database Notes
Lecture 25: Database Notes 36350, Fall 2014 12 November 2014 The examples here use http://www.stat.cmu.edu/~cshalizi/statcomp/ 14/lectures/23/baseball.db, which is derived from Lahman s baseball database
More informationReport Paper: MatLab/Database Connectivity
Report Paper: MatLab/Database Connectivity Samuel Moyle March 2003 Experiment Introduction This experiment was run following a visit to the University of Queensland, where a simulation engine has been
More informationCommon Patterns and Pitfalls for Implementing Algorithms in Spark. Hossein Falaki @mhfalaki hossein@databricks.com
Common Patterns and Pitfalls for Implementing Algorithms in Spark Hossein Falaki @mhfalaki hossein@databricks.com Challenges of numerical computation over big data When applying any algorithm to big data
More informationFast Analytics on Big Data with H20
Fast Analytics on Big Data with H20 0xdata.com, h2o.ai Tomas Nykodym, Petr Maj Team About H2O and 0xdata H2O is a platform for distributed in memory predictive analytics and machine learning Pure Java,
More informationTable of Contents. June 2010
June 2010 From: StatSoft Analytics White Papers To: Internal release Re: Performance comparison of STATISTICA Version 9 on multicore 64bit machines with current 64bit releases of SAS (Version 9.2) and
More informationSpark: Cluster Computing with Working Sets
Spark: Cluster Computing with Working Sets Outline Why? Mesos Resilient Distributed Dataset Spark & Scala Examples Uses Why? MapReduce deficiencies: Standard Dataflows are Acyclic Prevents Iterative Jobs
More informationDeal with big data in R using bigmemory package
Deal with big data in R using bigmemory package Xiaojuan Hao Department of Statistics University of NebraskaLincoln April 28, 2015 Background What Is Big Data Size (We focus on) Complexity Rate of growth
More informationSetting Up ALERE with Client/Server Data
Setting Up ALERE with Client/Server Data TIW Technology, Inc. November 2014 ALERE is a registered trademark of TIW Technology, Inc. The following are registered trademarks or trademarks: FoxPro, SQL Server,
More informationMANIPULATION OF LARGE DATABASES WITH "R"
MANIPULATION OF LARGE DATABASES WITH "R" Ana Maria DOBRE, Andreea GAGIU National Institute of Statistics, Bucharest Abstract Nowadays knowledge is power. In the informational era, the ability to manipulate
More informationOverview. Sparse Matrices for Large Data Modeling. Acknowledgements
Overview Sparse Matrices for Large Data Modeling Acknowledgements Martin Maechler ETH Zurich Switzerland maechler@rproject.org user!2007, Ames, Iowa, USA Aug 9 2007 Doug Bates has introduced me to sparse
More informationThe ff package: Handling Large Data Sets in R with Memory Mapped Pages of Binary Flat Files
UseR! 2007, Iowa State University, Ames, August 8108 2007 The ff package: Handling Large Data Sets in R with Memory Mapped Pages of Binary Flat Files D. Adler, O. Nenadić, W. Zucchini, C. Gläser Institute
More informationAdvanced Data Science on Spark
Advanced Data Science on Spark Reza Zadeh @Reza_Zadeh http://rezazadeh.com Data Science Problem Data growing faster than processing speeds Only solution is to parallelize on large clusters» Wide use in
More informationPackage dsmodellingclient
Package dsmodellingclient Maintainer Author Version 4.1.0 License GPL3 August 20, 2015 Title DataSHIELD client site functions for statistical modelling DataSHIELD
More informationMODULE 15 Clustering Large Datasets LESSON 34
MODULE 15 Clustering Large Datasets LESSON 34 Incremental Clustering Keywords: Single Database Scan, Leader, BIRCH, Tree 1 Clustering Large Datasets Pattern matrix It is convenient to view the input data
More informationDistributed Aggregation in Cloud Databases. By: Aparna Tiwari tiwaria@umail.iu.edu
Distributed Aggregation in Cloud Databases By: Aparna Tiwari tiwaria@umail.iu.edu ABSTRACT Data intensive applications rely heavily on aggregation functions for extraction of data according to user requirements.
More informationLecture 10: Dynamic Memory Allocation 1: Into the jaws of malloc()
CS61: Systems Programming and Machine Organization Harvard University, Fall 2009 Lecture 10: Dynamic Memory Allocation 1: Into the jaws of malloc() Prof. Matt Welsh October 6, 2009 Topics for today Dynamic
More informationIntroduction to Multivariate Models: Modeling Multivariate Outcomes with Mixed Model Repeated Measures Analyses
Introduction to Multivariate Models: Modeling Multivariate Outcomes with Mixed Model Repeated Measures Analyses Applied Multilevel Models for Cross Sectional Data Lecture 11 ICPSR Summer Workshop University
More informationScalable Data Science with Hadoop, Spark and R. Mario Inchiosa, PhD Principal Software Engineer Microsoft Data Group DSC 2016 July 2, 2016
Scalable Data Science with Hadoop, Spark and R Mario Inchiosa, PhD Principal Software Engineer Microsoft Data Group DSC 2016 July 2, 2016 Microsoft R Server Cloud Hadoop & Spark R Server portfolio R Server
More information2015 Workshops for Professors
SAS Education Grow with us Offered by the SAS Global Academic Program Supporting teaching, learning and research in higher education 2015 Workshops for Professors 1 Workshops for Professors As the market
More informationUnderstanding the Benefits of IBM SPSS Statistics Server
IBM SPSS Statistics Server Understanding the Benefits of IBM SPSS Statistics Server Contents: 1 Introduction 2 Performance 101: Understanding the drivers of better performance 3 Why performance is faster
More informationBase One's Rich Client Architecture
Base One's Rich Client Architecture Base One provides a unique approach for developing Internetenabled applications, combining both efficiency and ease of programming through its "Rich Client" architecture.
More informationCommunicating with the outside
Communicating with the outside Application Query Processor Indexes Storage Subsystem Concurrency Control Recovery Operating System Hardware [Processor(s), Disk(s), Memory] Accessing the Database Programming
More informationPackage neuralnet. February 20, 2015
Type Package Title Training of neural networks Version 1.32 Date 20120919 Package neuralnet February 20, 2015 Author Stefan Fritsch, Frauke Guenther , following earlier work
More informationDistributed R for Big Data
Distributed R for Big Data Indrajit Roy HP Vertica Development Team Abstract Distributed R simplifies largescale analysis. It extends R. R is a singlethreaded environment which limits its utility for
More informationGamma Distribution Fitting
Chapter 552 Gamma Distribution Fitting Introduction This module fits the gamma probability distributions to a complete or censored set of individual or grouped data values. It outputs various statistics
More informationHighPerformance Oracle: Proven Methods for Achieving Optimum Performance and Availability
About the Author Geoff Ingram (mailto:geoff@dbcool.com) is a UKbased exoracle product developer who has worked as an independent Oracle consultant since leaving Oracle Corporation in the midnineties.
More informationLarge Datasets and You: A Field Guide
Large Datasets and You: A Field Guide Matthew Blackwell m.blackwell@rochester.edu Maya Sen msen@ur.rochester.edu August 3, 2012 A wind of streaming data, social data and unstructured data is knocking at
More informationParallel Computing for Data Science
Parallel Computing for Data Science With Examples in R, C++ and CUDA Norman Matloff University of California, Davis USA (g) CRC Press Taylor & Francis Group Boca Raton London New York CRC Press is an imprint
More informationWhitepaper: performance of SqlBulkCopy
We SOLVE COMPLEX PROBLEMS of DATA MODELING and DEVELOP TOOLS and solutions to let business perform best through data analysis Whitepaper: performance of SqlBulkCopy This whitepaper provides an analysis
More informationAdvanced Big Data Analytics with R and Hadoop
REVOLUTION ANALYTICS WHITE PAPER Advanced Big Data Analytics with R and Hadoop 'Big Data' Analytics as a Competitive Advantage Big Analytics delivers competitive advantage in two ways compared to the traditional
More informationOracle Database InMemory The Next Big Thing
Oracle Database InMemory The Next Big Thing Maria Colgan Master Product Manager #DBIM12c Why is Oracle do this Oracle Database InMemory Goals Real Time Analytics Accelerate Mixed Workload OLTP No Changes
More informationApplicationCentric Analysis Helps Maximize the Value of Wireshark
ApplicationCentric Analysis Helps Maximize the Value of Wireshark The cost of freeware Protocol analysis has long been viewed as the last line of defense when it comes to resolving nagging network and
More informationlm {stats} R Documentation
lm {stats} R Documentation Fitting Linear Models Description lm is used to fit linear models. It can be used to carry out regression, single stratum analysis of variance and analysis of covariance (although
More informationOverview of Databases On MacOS. Karl Kuehn Automation Engineer RethinkDB
Overview of Databases On MacOS Karl Kuehn Automation Engineer RethinkDB Session Goals Introduce Database concepts Show example players Not Goals: Cover nonmacos systems (Oracle) Teach you SQL Answer what
More informationHardware Configuration Guide
Hardware Configuration Guide Contents Contents... 1 Annotation... 1 Factors to consider... 2 Machine Count... 2 Data Size... 2 Data Size Total... 2 Daily Backup Data Size... 2 Unique Data Percentage...
More informationComputing with large data sets
Computing with large data sets Richard Bonneau, spring 009 minilecture 1(week 7): big data, R databases, RSQLite, DBI, clara different reasons for using databases There are a multitude of reasons for
More informationSAP HANA  Main Memory Technology: A Challenge for Development of Business Applications. Jürgen Primsch, SAP AG July 2011
SAP HANA  Main Memory Technology: A Challenge for Development of Business Applications Jürgen Primsch, SAP AG July 2011 Why InMemory? Information at the Speed of Thought Imagine access to business data,
More informationParallelization Strategies for Multicore Data Analysis
Parallelization Strategies for Multicore Data Analysis WeiChen Chen 1 Russell Zaretzki 2 1 University of Tennessee, Dept of EEB 2 University of Tennessee, Dept. Statistics, Operations, and Management
More informationIncidentMonitor Server Specification Datasheet
IncidentMonitor Server Specification Datasheet Prepared by Monitor 247 Inc October 1, 2015 Contact details: sales@monitor247.com North America: +1 416 410.2716 / +1 866 364.2757 Europe: +31 088 008.4600
More informationUsing the SQL Server Linked Server Capability
Using the SQL Server Linked Server Capability SQL Server s Linked Server feature enables fast and easy integration of SQL Server data and non SQL Server data, directly in the SQL Server engine itself.
More informationORACLE OLAP. Oracle OLAP is embedded in the Oracle Database kernel and runs in the same database process
ORACLE OLAP KEY FEATURES AND BENEFITS FAST ANSWERS TO TOUGH QUESTIONS EASILY KEY FEATURES & BENEFITS World class analytic engine Superior query performance Simple SQL access to advanced analytics Enhanced
More informationPackage biganalytics
Package biganalytics February 19, 2015 Version 1.1.1 Date 20120920 Title A library of utilities for big.matrix objects of package bigmemory. Author John W. Emerson and Michael
More informationProfile Likelihood Confidence Intervals for GLM s
Profile Likelihood Confidence Intervals for GLM s The standard procedure for computing a confidence interval (CI) for a parameter in a generalized linear model is by the formula: estimate ± percentile
More informationBENCHMARKING CLOUD DATABASES CASE STUDY on HBASE, HADOOP and CASSANDRA USING YCSB
BENCHMARKING CLOUD DATABASES CASE STUDY on HBASE, HADOOP and CASSANDRA USING YCSB Planet Size Data!? Gartner s 10 key IT trends for 2012 unstructured data will grow some 80% over the course of the next
More informationHadoop Fair Scheduler Design Document
Hadoop Fair Scheduler Design Document October 18, 2010 Contents 1 Introduction 2 2 Fair Scheduler Goals 2 3 Scheduler Features 2 3.1 Pools........................................ 2 3.2 Minimum Shares.................................
More informationFactoring. Factoring 1
Factoring Factoring 1 Factoring Security of RSA algorithm depends on (presumed) difficulty of factoring o Given N = pq, find p or q and RSA is broken o Rabin cipher also based on factoring Factoring like
More informationPackage bigrf. February 19, 2015
Version 0.111 Date 20140516 Package bigrf February 19, 2015 Title Big Random Forests: Classification and Regression Forests for Large Data Sets Maintainer Aloysius Lim OS_type
More informationSQL Server Array Library 201011 László Dobos, Alexander S. Szalay
SQL Server Array Library 201011 László Dobos, Alexander S. Szalay The Johns Hopkins University, Department of Physics and Astronomy Eötvös University, Department of Physics of Complex Systems http://voservices.net/sqlarray,
More informationBig data in R EPIC 2015
Big data in R EPIC 2015 Big Data: the new 'The Future' In which Forbes magazine finds common ground with Nancy Krieger (for the first time ever?), by arguing the need for theorydriven analysis This future
More informationArchitectures for Big Data Analytics A database perspective
Architectures for Big Data Analytics A database perspective Fernando Velez Director of Product Management Enterprise Information Management, SAP June 2013 Outline Big Data Analytics Requirements Spectrum
More informationImproving the Performance of the Model Repository
Improving the Performance of the Model Repository 19932016 Informatica LLC. No part of this document may be reproduced or transmitted in any form, by any means (electronic, photocopying, recording or
More informationCloud Computing at Google. Architecture
Cloud Computing at Google Google File System Web Systems and Algorithms Google Chris Brooks Department of Computer Science University of San Francisco Google has developed a layered system to handle webscale
More informationVirtualCenter Database Performance for Microsoft SQL Server 2005 VirtualCenter 2.5
Performance Study VirtualCenter Database Performance for Microsoft SQL Server 2005 VirtualCenter 2.5 VMware VirtualCenter uses a database to store metadata on the state of a VMware Infrastructure environment.
More informationFeliss: Flexible distributed computing framework with lightweight checkpointing. Takuya Araki, Kazuyo Narita and Hiroshi Tamano NEC Corporation
Feliss: Flexible distributed computing framework with lightweight checkpointing Takuya Araki, Kazuyo Narita and Hiroshi Tamano NEC Corporation 1 Background Big Data analysis is becoming common sensors,
More informationDistributed computing: index building and use
Distributed computing: index building and use Distributed computing Goals Distributing computation across several machines to Do one computation faster  latency Do more computations in given time  throughput
More informationHow to recover a failed Storage Spaces
www.storagespacesrecovery.com How to recover a failed Storage Spaces ReclaiMe Storage Spaces Recovery User Manual 2013 www.storagespacesrecovery.com Contents Overview... 4 Storage Spaces concepts and
More informationChapter15  Replication in SQL Server
Important Terminologies: What is Replication? Replication is the process where data is copied between databases on the same server or different servers connected by LANs, WANs, or the Internet. Microsoft
More informationdatabase abstraction layer database abstraction layers in PHP Lukas Smith BackendMedia smith@backendmedia.com
Lukas Smith database abstraction layers in PHP BackendMedia 1 Overview Introduction Motivation PDO extension PEAR::MDB2 Client API SQL syntax SQL concepts Result sets Error handling High level features
More informationTechniques Covered in this Class. Inverted List Compression Techniques. Recap: Search Engine Query Processing. Recap: Search Engine Query Processing
Recap: Search Engine Query Processing Basically, to process a query we need to traverse the inverted lists of the query terms Lists are very long and are stored on disks Challenge: traverse lists as quickly
More informationMoving from DBF to SQL Server Pros and Cons
Moving from DBF to SQL Server Pros and Cons Overview This document discusses the issues related to migrating a VFP DBF application to use SQL Server. A VFP DBF application uses native Visual FoxPro (VFP)
More informationwww.dotnetsparkles.wordpress.com
Database Design Considerations Designing a database requires an understanding of both the business functions you want to model and the database concepts and features used to represent those business functions.
More informationThe PageRank Citation Ranking: Bring Order to the Web
The PageRank Citation Ranking: Bring Order to the Web presented by: Xiaoxi Pang 25.Nov 2010 1 / 20 Outline Introduction A ranking for every page on the Web Implementation Convergence Properties Personalized
More informationWhite Paper. Optimizing the Performance Of MySQL Cluster
White Paper Optimizing the Performance Of MySQL Cluster Table of Contents Introduction and Background Information... 2 Optimal Applications for MySQL Cluster... 3 Identifying the Performance Issues.....
More informationRecovery Principles in MySQL Cluster 5.1
Recovery Principles in MySQL Cluster 5.1 Mikael Ronström Senior Software Architect MySQL AB 1 Outline of Talk Introduction of MySQL Cluster in version 4.1 and 5.0 Discussion of requirements for MySQL Cluster
More informationSQL Server An Overview
SQL Server An Overview SQL Server Microsoft SQL Server is designed to work effectively in a number of environments: As a twotier or multitier client/server database system As a desktop database system
More informationDistributed File Systems
Distributed File Systems Paul Krzyzanowski Rutgers University October 28, 2012 1 Introduction The classic network file systems we examined, NFS, CIFS, AFS, Coda, were designed as clientserver applications.
More informationVector algebra Christian Miller CS Fall 2011
Vector algebra Christian Miller CS 354  Fall 2011 Vector algebra A system commonly used to describe space Vectors, linear operators, tensors, etc. Used to build classical physics and the vast majority
More informationOracle BI EE Implementation on Netezza. Prepared by SureShot Strategies, Inc.
Oracle BI EE Implementation on Netezza Prepared by SureShot Strategies, Inc. The goal of this paper is to give an insight to Netezza architecture and implementation experience to strategize Oracle BI EE
More informationQuotient Filters: Approximate Membership Queries on the GPU
Quotient Filters: Approximate Membership Queries on the GPU Afton Geil University of California, Davis GTC 2016 Outline What are approximate membership queries and how are they used? Background on quotient
More informationIn Memory Accelerator for MongoDB
In Memory Accelerator for MongoDB Yakov Zhdanov, Director R&D GridGain Systems GridGain: In Memory Computing Leader 5 years in production 100s of customers & users Starts every 10 secs worldwide Over 15,000,000
More informationMatrix Multiplication
Matrix Multiplication CPS343 Parallel and High Performance Computing Spring 2016 CPS343 (Parallel and HPC) Matrix Multiplication Spring 2016 1 / 32 Outline 1 Matrix operations Importance Dense and sparse
More informationData Mining in the Swamp
WHITE PAPER Page 1 of 8 Data Mining in the Swamp Taming Unruly Data with Cloud Computing By John Brothers Business Intelligence is all about making better decisions from the data you have. However, all
More informationClassification and Regression by randomforest
Vol. 2/3, December 02 18 Classification and Regression by randomforest Andy Liaw and Matthew Wiener Introduction Recently there has been a lot of interest in ensemble learning methods that generate many
More informationEFFICIENT EXTERNAL SORTING ON FLASH MEMORY EMBEDDED DEVICES
ABSTRACT EFFICIENT EXTERNAL SORTING ON FLASH MEMORY EMBEDDED DEVICES Tyler Cossentine and Ramon Lawrence Department of Computer Science, University of British Columbia Okanagan Kelowna, BC, Canada tcossentine@gmail.com
More informationHadoop Ecosystem B Y R A H I M A.
Hadoop Ecosystem B Y R A H I M A. History of Hadoop Hadoop was created by Doug Cutting, the creator of Apache Lucene, the widely used text search library. Hadoop has its origins in Apache Nutch, an open
More informationPenalized regression: Introduction
Penalized regression: Introduction Patrick Breheny August 30 Patrick Breheny BST 764: Applied Statistical Modeling 1/19 Maximum likelihood Much of 20thcentury statistics dealt with maximum likelihood
More informationSharding with postgres_fdw
Sharding with postgres_fdw Postgres Open 2013 Chicago Stephen Frost sfrost@snowman.net Resonate, Inc. Digital Media PostgreSQL Hadoop techjobs@resonateinsights.com http://www.resonateinsights.com Stephen
More informationLecture 10: HBase! Claudia Hauff (Web Information Systems)! ti2736bewi@tudelft.nl
Big Data Processing, 2014/15 Lecture 10: HBase!! Claudia Hauff (Web Information Systems)! ti2736bewi@tudelft.nl 1 Course content Introduction Data streams 1 & 2 The MapReduce paradigm Looking behind the
More informationDURGA SOFTWARE SOLUTUIONS,S.R NAGAR,HYDERABAD. Ph:9246212143,04064512786. Abstract
Abstract The problem that we specify is that now day it is too difficult for both writing and maintaining records manually. It takes lots of time for writing records manually. Even there is chance of missing
More informationPackage changepoint. R topics documented: November 9, 2015. Type Package Title Methods for Changepoint Detection Version 2.
Type Package Title Methods for Changepoint Detection Version 2.2 Date 20151023 Package changepoint November 9, 2015 Maintainer Rebecca Killick Implements various mainstream and
More informationrm(list=ls()) library(sqldf) system.time({large = read.csv.sql("large.csv")}) #172.97 seconds, 4.23GB of memory used by R
Big Data in R Importing data into R: 1.75GB file Table 1: Comparison of importing data into R Time Taken Packages Functions (second) Remark/Note base read.csv > 2,394 My machine (8GB of memory) ran out
More informationFlat Clustering KMeans Algorithm
Flat Clustering KMeans Algorithm 1. Purpose. Clustering algorithms group a set of documents into subsets or clusters. The cluster algorithms goal is to create clusters that are coherent internally, but
More informationOptimization of sampling strata with the SamplingStrata package
Optimization of sampling strata with the SamplingStrata package Package version 1.1 Giulio Barcaroli January 12, 2016 Abstract In stratified random sampling the problem of determining the optimal size
More informationDATABASE VIRTUALIZATION AND INSTANT CLONING WHITE PAPER
DATABASE VIRTUALIZATION AND INSTANT CLONING TABLE OF CONTENTS Brief...3 Introduction...3 Solutions...4 Technologies....5 Database Virtualization...7 Database Virtualization Examples...9 Summary....9 Appendix...
More informationInfiniteGraph: The Distributed Graph Database
A Performance and Distributed Performance Benchmark of InfiniteGraph and a Leading Open Source Graph Database Using Synthetic Data Objectivity, Inc. 640 West California Ave. Suite 240 Sunnyvale, CA 94086
More information1 File Processing Systems
COMP 378 Database Systems Notes for Chapter 1 of Database System Concepts Introduction A database management system (DBMS) is a collection of data and an integrated set of programs that access that data.
More informationFramework 8.1. Genesys Administrator Extension. Deployment Guide
Framework 8.1 Genesys Administrator Extension Deployment Guide The information contained herein is proprietary and confidential and cannot be disclosed or duplicated without the prior written consent of
More informationOperating Systems: Internals and Design Principles. Chapter 12 File Management Seventh Edition By William Stallings
Operating Systems: Internals and Design Principles Chapter 12 File Management Seventh Edition By William Stallings Operating Systems: Internals and Design Principles If there is one singular characteristic
More informationGWAS Data Cleaning. GENEVA Coordinating Center Department of Biostatistics University of Washington. January 13, 2016.
GWAS Data Cleaning GENEVA Coordinating Center Department of Biostatistics University of Washington January 13, 2016 Contents 1 Overview 2 2 Preparing Data 3 2.1 Data formats used in GWASTools............................
More informationBuilding risk prediction models  with a focus on GenomeWide Association Studies. Charles Kooperberg
Building risk prediction models  with a focus on GenomeWide Association Studies Risk prediction models Based on data: (D i, X i1,..., X ip ) i = 1,..., n we like to fit a model P(D = 1 X 1,..., X p )
More informationOperating Systems. Virtual Memory
Operating Systems Virtual Memory Virtual Memory Topics. Memory Hierarchy. Why Virtual Memory. Virtual Memory Issues. Virtual Memory Solutions. Locality of Reference. Virtual Memory with Segmentation. Page
More information